OpenAIR @ RGU >
Design and Technology >
Computing >
Journal articles (Computing) >

Please use this identifier to cite or link to this item:
This item has been viewed 4 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
Song Web Intelligence and Agent Systems 2007.pdf220.5 kBAdobe PDFView/Open
Title: Relation discovery from web data for competency management.
Authors: Zhu, Jianhan
Goncalves, Alexandre L.
Uren, Victoria
Motta, Enrico
Pacheco, Roberto
Eisenstadt, Marc
Song, Dawei
Keywords: Relation discovery
Named entity recognition
Issue Date: Dec-2007
Publisher: IOS Press
Citation: ZHU, J., GONCALVES, A. L., UREN, V. S., MOTTA, E., PACHECO, R., EISENSTADT, M. and SONG, D., 2007. Relation discovery from web data for competency management. Web Intelligence and Agent Systems, 5 (4), pp. 405-417.
Abstract: In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.
ISSN: 1570-1263
Appears in Collections:Journal articles (Computing)

All items in OpenAIR are protected by copyright, with all rights reserved.


   Disclaimer | Freedom of Information | Privacy Statement |Copyright ©2012 Robert Gordon University, Garthdee House, Garthdee Road, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781